Overview

Dataset statistics

Number of variables14
Number of observations138
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.2 KiB
Average record size in memory112.9 B

Variable types

Numeric13
Categorical1

Alerts

alcohol is highly overall correlated with color_intensity and 2 other fieldsHigh correlation
color_intensity is highly overall correlated with alcohol and 1 other fieldsHigh correlation
flavanoids is highly overall correlated with hue and 5 other fieldsHigh correlation
hue is highly overall correlated with flavanoids and 2 other fieldsHigh correlation
magnesium is highly overall correlated with prolineHigh correlation
malic_acid is highly overall correlated with hueHigh correlation
nonflavanoid_phenols is highly overall correlated with flavanoids and 1 other fieldsHigh correlation
od280/od315_of_diluted_wines is highly overall correlated with flavanoids and 4 other fieldsHigh correlation
proanthocyanins is highly overall correlated with flavanoids and 2 other fieldsHigh correlation
proline is highly overall correlated with alcohol and 2 other fieldsHigh correlation
target is highly overall correlated with alcohol and 6 other fieldsHigh correlation
total_phenols is highly overall correlated with flavanoids and 3 other fieldsHigh correlation

Reproduction

Analysis started2024-03-03 14:05:25.762766
Analysis finished2024-03-03 14:06:13.341194
Duration47.58 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

alcohol
Real number (ℝ)

HIGH CORRELATION 

Distinct102
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.977101
Minimum11.03
Maximum14.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-03T14:06:13.551591image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum11.03
5-th percentile11.7705
Q112.37
median13.04
Q313.56
95-th percentile14.2405
Maximum14.83
Range3.8
Interquartile range (IQR)1.19

Descriptive statistics

Standard deviation0.8025927
Coefficient of variation (CV)0.061846839
Kurtosis-0.6820526
Mean12.977101
Median Absolute Deviation (MAD)0.63
Skewness0.00249994
Sum1790.84
Variance0.64415504
MonotonicityNot monotonic
2024-03-03T14:06:13.978292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.05 6
 
4.3%
12.37 5
 
3.6%
12.08 4
 
2.9%
12.29 3
 
2.2%
12.25 3
 
2.2%
12 3
 
2.2%
12.72 2
 
1.4%
12.42 2
 
1.4%
13.86 2
 
1.4%
13.24 2
 
1.4%
Other values (92) 106
76.8%
ValueCountFrequency (%)
11.03 1
0.7%
11.41 1
0.7%
11.45 1
0.7%
11.46 1
0.7%
11.61 1
0.7%
11.64 1
0.7%
11.66 1
0.7%
11.79 1
0.7%
11.81 1
0.7%
11.82 2
1.4%
ValueCountFrequency (%)
14.83 1
0.7%
14.75 1
0.7%
14.39 1
0.7%
14.38 1
0.7%
14.37 1
0.7%
14.34 1
0.7%
14.3 1
0.7%
14.23 1
0.7%
14.22 2
1.4%
14.21 1
0.7%

malic_acid
Real number (ℝ)

HIGH CORRELATION 

Distinct105
Distinct (%)76.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3294203
Minimum0.74
Maximum5.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-03T14:06:14.409528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.74
5-th percentile0.9885
Q11.61
median1.81
Q33.1575
95-th percentile4.3705
Maximum5.8
Range5.06
Interquartile range (IQR)1.5475

Descriptive statistics

Standard deviation1.0995698
Coefficient of variation (CV)0.4720358
Kurtosis0.1061509
Mean2.3294203
Median Absolute Deviation (MAD)0.475
Skewness0.95870793
Sum321.46
Variance1.2090537
MonotonicityNot monotonic
2024-03-03T14:06:14.909656image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.73 7
 
5.1%
1.81 4
 
2.9%
1.67 3
 
2.2%
1.51 3
 
2.2%
1.68 3
 
2.2%
1.61 3
 
2.2%
1.53 3
 
2.2%
1.9 2
 
1.4%
1.75 2
 
1.4%
1.83 2
 
1.4%
Other values (95) 106
76.8%
ValueCountFrequency (%)
0.74 1
0.7%
0.89 1
0.7%
0.9 1
0.7%
0.92 1
0.7%
0.94 2
1.4%
0.98 1
0.7%
0.99 1
0.7%
1.09 1
0.7%
1.13 2
1.4%
1.17 1
0.7%
ValueCountFrequency (%)
5.8 1
0.7%
5.51 1
0.7%
5.04 1
0.7%
4.72 1
0.7%
4.61 1
0.7%
4.6 1
0.7%
4.43 1
0.7%
4.36 1
0.7%
4.31 1
0.7%
4.28 1
0.7%

ash
Real number (ℝ)

Distinct69
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3645652
Minimum1.36
Maximum3.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-03T14:06:15.329581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.36
5-th percentile1.92
Q12.2
median2.36
Q32.56
95-th percentile2.7315
Maximum3.22
Range1.86
Interquartile range (IQR)0.36

Descriptive statistics

Standard deviation0.26906738
Coefficient of variation (CV)0.11379148
Kurtosis1.0609598
Mean2.3645652
Median Absolute Deviation (MAD)0.17
Skewness-0.28336276
Sum326.31
Variance0.072397255
MonotonicityNot monotonic
2024-03-03T14:06:15.748568image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.7 6
 
4.3%
2.3 5
 
3.6%
2.36 5
 
3.6%
2.2 5
 
3.6%
2.48 4
 
2.9%
2.32 4
 
2.9%
2.28 4
 
2.9%
2.64 3
 
2.2%
2.62 3
 
2.2%
2.17 3
 
2.2%
Other values (59) 96
69.6%
ValueCountFrequency (%)
1.36 1
0.7%
1.71 1
0.7%
1.75 1
0.7%
1.82 1
0.7%
1.88 1
0.7%
1.9 1
0.7%
1.92 2
1.4%
1.95 1
0.7%
1.98 2
1.4%
1.99 1
0.7%
ValueCountFrequency (%)
3.22 1
 
0.7%
2.87 1
 
0.7%
2.86 1
 
0.7%
2.84 1
 
0.7%
2.78 1
 
0.7%
2.75 1
 
0.7%
2.74 1
 
0.7%
2.73 1
 
0.7%
2.72 2
 
1.4%
2.7 6
4.3%

alcalinity_of_ash
Real number (ℝ)

Distinct54
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.619565
Minimum10.6
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-03T14:06:16.162223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum10.6
5-th percentile14.51
Q117.2
median19.55
Q321.5
95-th percentile25
Maximum30
Range19.4
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation3.3685072
Coefficient of variation (CV)0.17169123
Kurtosis0.50297475
Mean19.619565
Median Absolute Deviation (MAD)2.05
Skewness0.07742498
Sum2707.5
Variance11.346841
MonotonicityNot monotonic
2024-03-03T14:06:16.675663image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 12
 
8.7%
21 10
 
7.2%
21.5 8
 
5.8%
16 8
 
5.8%
22.5 7
 
5.1%
19 7
 
5.1%
19.5 6
 
4.3%
18.5 6
 
4.3%
25 5
 
3.6%
16.8 5
 
3.6%
Other values (44) 64
46.4%
ValueCountFrequency (%)
10.6 1
0.7%
11.2 1
0.7%
11.4 1
0.7%
12.4 1
0.7%
13.2 1
0.7%
14 2
1.4%
14.6 1
0.7%
14.8 1
0.7%
15.2 2
1.4%
15.5 1
0.7%
ValueCountFrequency (%)
30 1
 
0.7%
28.5 1
 
0.7%
27 1
 
0.7%
26.5 1
 
0.7%
25.5 1
 
0.7%
25 5
3.6%
24.5 2
 
1.4%
24 4
2.9%
23.6 1
 
0.7%
23.5 1
 
0.7%

magnesium
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.971014
Minimum70
Maximum162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-03T14:06:17.105821image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile81.85
Q188
median98
Q3108
95-th percentile127.15
Maximum162
Range92
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.991454
Coefficient of variation (CV)0.149958
Kurtosis2.1103623
Mean99.971014
Median Absolute Deviation (MAD)10
Skewness1.1432371
Sum13796
Variance224.74368
MonotonicityNot monotonic
2024-03-03T14:06:17.512044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
86 11
 
8.0%
88 10
 
7.2%
96 7
 
5.1%
102 6
 
4.3%
101 6
 
4.3%
112 6
 
4.3%
85 5
 
3.6%
98 5
 
3.6%
89 4
 
2.9%
106 4
 
2.9%
Other values (39) 74
53.6%
ValueCountFrequency (%)
70 1
 
0.7%
78 2
 
1.4%
80 3
 
2.2%
81 1
 
0.7%
82 1
 
0.7%
84 3
 
2.2%
85 5
3.6%
86 11
8.0%
87 3
 
2.2%
88 10
7.2%
ValueCountFrequency (%)
162 1
0.7%
151 1
0.7%
139 1
0.7%
136 1
0.7%
134 1
0.7%
132 1
0.7%
128 1
0.7%
127 1
0.7%
124 1
0.7%
123 1
0.7%

total_phenols
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2828986
Minimum0.98
Maximum3.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-03T14:06:17.878647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.98
5-th percentile1.38
Q11.705
median2.375
Q32.8
95-th percentile3.2075
Maximum3.88
Range2.9
Interquartile range (IQR)1.095

Descriptive statistics

Standard deviation0.62570743
Coefficient of variation (CV)0.27408464
Kurtosis-0.78757352
Mean2.2828986
Median Absolute Deviation (MAD)0.48
Skewness0.055622215
Sum315.04
Variance0.39150979
MonotonicityNot monotonic
2024-03-03T14:06:18.360324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2 8
 
5.8%
2.8 6
 
4.3%
2.6 5
 
3.6%
3 5
 
3.6%
1.38 4
 
2.9%
2.85 4
 
2.9%
1.65 3
 
2.2%
2.95 3
 
2.2%
1.48 3
 
2.2%
2 3
 
2.2%
Other values (70) 94
68.1%
ValueCountFrequency (%)
0.98 1
 
0.7%
1.1 1
 
0.7%
1.25 1
 
0.7%
1.28 1
 
0.7%
1.3 1
 
0.7%
1.38 4
2.9%
1.39 2
1.4%
1.4 2
1.4%
1.41 1
 
0.7%
1.45 2
1.4%
ValueCountFrequency (%)
3.88 1
0.7%
3.85 1
0.7%
3.5 1
0.7%
3.38 1
0.7%
3.3 1
0.7%
3.27 1
0.7%
3.25 1
0.7%
3.2 1
0.7%
3.18 1
0.7%
3.15 1
0.7%

flavanoids
Real number (ℝ)

HIGH CORRELATION 

Distinct108
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9989855
Minimum0.34
Maximum3.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-03T14:06:18.735867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.5085
Q11.2275
median2.155
Q32.7875
95-th percentile3.305
Maximum3.74
Range3.4
Interquartile range (IQR)1.56

Descriptive statistics

Standard deviation0.95743673
Coefficient of variation (CV)0.47896132
Kurtosis-1.2556921
Mean1.9989855
Median Absolute Deviation (MAD)0.795
Skewness-0.18582169
Sum275.86
Variance0.91668509
MonotonicityNot monotonic
2024-03-03T14:06:19.174606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.65 4
 
2.9%
2.68 3
 
2.2%
0.6 3
 
2.2%
3.39 2
 
1.4%
1.36 2
 
1.4%
2.53 2
 
1.4%
0.66 2
 
1.4%
1.59 2
 
1.4%
2.79 2
 
1.4%
1.69 2
 
1.4%
Other values (98) 114
82.6%
ValueCountFrequency (%)
0.34 1
0.7%
0.47 2
1.4%
0.48 1
0.7%
0.49 1
0.7%
0.5 2
1.4%
0.51 1
0.7%
0.52 1
0.7%
0.55 1
0.7%
0.56 1
0.7%
0.57 1
0.7%
ValueCountFrequency (%)
3.74 1
0.7%
3.69 1
0.7%
3.54 1
0.7%
3.49 1
0.7%
3.4 1
0.7%
3.39 2
1.4%
3.29 1
0.7%
3.27 1
0.7%
3.25 1
0.7%
3.24 1
0.7%

nonflavanoid_phenols
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3592029
Minimum0.13
Maximum0.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-03T14:06:19.601133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile0.1985
Q10.27
median0.34
Q30.43
95-th percentile0.58
Maximum0.63
Range0.5
Interquartile range (IQR)0.16

Descriptive statistics

Standard deviation0.12000037
Coefficient of variation (CV)0.33407407
Kurtosis-0.59866936
Mean0.3592029
Median Absolute Deviation (MAD)0.08
Skewness0.41380057
Sum49.57
Variance0.01440009
MonotonicityNot monotonic
2024-03-03T14:06:20.043714image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0.29 9
 
6.5%
0.43 9
 
6.5%
0.3 8
 
5.8%
0.26 8
 
5.8%
0.37 7
 
5.1%
0.34 7
 
5.1%
0.4 7
 
5.1%
0.53 6
 
4.3%
0.32 6
 
4.3%
0.27 6
 
4.3%
Other values (26) 65
47.1%
ValueCountFrequency (%)
0.13 1
 
0.7%
0.14 2
 
1.4%
0.17 3
 
2.2%
0.19 1
 
0.7%
0.2 2
 
1.4%
0.21 5
3.6%
0.22 4
2.9%
0.24 5
3.6%
0.25 1
 
0.7%
0.26 8
5.8%
ValueCountFrequency (%)
0.63 3
2.2%
0.61 2
 
1.4%
0.6 1
 
0.7%
0.58 3
2.2%
0.55 1
 
0.7%
0.53 6
4.3%
0.52 4
2.9%
0.5 5
3.6%
0.48 3
2.2%
0.47 2
 
1.4%

proanthocyanins
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)62.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6089855
Minimum0.42
Maximum3.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-03T14:06:20.401493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile0.7225
Q11.25
median1.59
Q31.9575
95-th percentile2.7675
Maximum3.58
Range3.16
Interquartile range (IQR)0.7075

Descriptive statistics

Standard deviation0.58386491
Coefficient of variation (CV)0.36287767
Kurtosis0.62506992
Mean1.6089855
Median Absolute Deviation (MAD)0.36
Skewness0.52827847
Sum222.04
Variance0.34089823
MonotonicityNot monotonic
2024-03-03T14:06:20.786476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.35 6
 
4.3%
1.87 5
 
3.6%
1.46 4
 
2.9%
1.66 4
 
2.9%
1.98 4
 
2.9%
1.25 3
 
2.2%
2.08 3
 
2.2%
1.56 3
 
2.2%
1.14 3
 
2.2%
1.63 3
 
2.2%
Other values (76) 100
72.5%
ValueCountFrequency (%)
0.42 2
1.4%
0.55 1
0.7%
0.62 1
0.7%
0.64 2
1.4%
0.68 1
0.7%
0.73 1
0.7%
0.75 1
0.7%
0.8 2
1.4%
0.81 1
0.7%
0.83 1
0.7%
ValueCountFrequency (%)
3.58 1
 
0.7%
3.28 1
 
0.7%
2.91 2
1.4%
2.81 3
2.2%
2.76 1
 
0.7%
2.7 1
 
0.7%
2.5 1
 
0.7%
2.49 1
 
0.7%
2.45 1
 
0.7%
2.38 1
 
0.7%

color_intensity
Real number (ℝ)

HIGH CORRELATION 

Distinct110
Distinct (%)79.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.997029
Minimum1.28
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-03T14:06:21.171655image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.28
5-th percentile2.06
Q13.0575
median4.69
Q36.1825
95-th percentile9.7299999
Maximum13
Range11.72
Interquartile range (IQR)3.125

Descriptive statistics

Standard deviation2.3291204
Coefficient of variation (CV)0.46610105
Kurtosis0.66826206
Mean4.997029
Median Absolute Deviation (MAD)1.585
Skewness0.92174459
Sum689.59
Variance5.424802
MonotonicityNot monotonic
2024-03-03T14:06:21.585496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.6 4
 
2.9%
5 3
 
2.2%
2.9 3
 
2.2%
5.4 3
 
2.2%
5.1 3
 
2.2%
3.05 3
 
2.2%
2.45 2
 
1.4%
3.4 2
 
1.4%
1.95 2
 
1.4%
2.65 2
 
1.4%
Other values (100) 111
80.4%
ValueCountFrequency (%)
1.28 1
0.7%
1.74 1
0.7%
1.9 1
0.7%
1.95 2
1.4%
2 1
0.7%
2.06 2
1.4%
2.08 1
0.7%
2.12 1
0.7%
2.2 1
0.7%
2.3 1
0.7%
ValueCountFrequency (%)
13 1
0.7%
11.75 1
0.7%
10.8 1
0.7%
10.52 1
0.7%
10.26 1
0.7%
10.2 1
0.7%
9.899999 1
0.7%
9.7 1
0.7%
9.4 1
0.7%
8.9 1
0.7%

hue
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.94953623
Minimum0.48
Maximum1.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-03T14:06:22.049715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.48
5-th percentile0.57
Q10.7825
median0.96
Q31.09
95-th percentile1.2845
Maximum1.71
Range1.23
Interquartile range (IQR)0.3075

Descriptive statistics

Standard deviation0.22358242
Coefficient of variation (CV)0.23546486
Kurtosis0.022526443
Mean0.94953623
Median Absolute Deviation (MAD)0.155
Skewness0.059540318
Sum131.036
Variance0.049989097
MonotonicityNot monotonic
2024-03-03T14:06:22.615405image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.04 7
 
5.1%
0.96 5
 
3.6%
1.12 5
 
3.6%
1.23 4
 
2.9%
0.57 4
 
2.9%
0.89 4
 
2.9%
1.05 4
 
2.9%
1.19 4
 
2.9%
0.75 3
 
2.2%
0.92 3
 
2.2%
Other values (59) 95
68.8%
ValueCountFrequency (%)
0.48 1
 
0.7%
0.54 1
 
0.7%
0.55 1
 
0.7%
0.56 2
1.4%
0.57 4
2.9%
0.58 2
1.4%
0.59 2
1.4%
0.6 1
 
0.7%
0.61 1
 
0.7%
0.62 1
 
0.7%
ValueCountFrequency (%)
1.71 1
 
0.7%
1.42 1
 
0.7%
1.38 1
 
0.7%
1.36 1
 
0.7%
1.33 1
 
0.7%
1.31 2
1.4%
1.28 2
1.4%
1.25 2
1.4%
1.23 4
2.9%
1.22 1
 
0.7%

od280/od315_of_diluted_wines
Real number (ℝ)

HIGH CORRELATION 

Distinct101
Distinct (%)73.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6049275
Minimum1.27
Maximum3.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-03T14:06:23.115510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.27
5-th percentile1.4625
Q11.97
median2.78
Q33.155
95-th percentile3.533
Maximum3.92
Range2.65
Interquartile range (IQR)1.185

Descriptive statistics

Standard deviation0.68603935
Coefficient of variation (CV)0.26336216
Kurtosis-1.0442341
Mean2.6049275
Median Absolute Deviation (MAD)0.49
Skewness-0.34519963
Sum359.48
Variance0.47064999
MonotonicityNot monotonic
2024-03-03T14:06:23.570529image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.78 4
 
2.9%
2.87 4
 
2.9%
1.82 4
 
2.9%
1.75 3
 
2.2%
1.33 3
 
2.2%
3.33 3
 
2.2%
3 3
 
2.2%
2.31 3
 
2.2%
2.26 2
 
1.4%
2.84 2
 
1.4%
Other values (91) 107
77.5%
ValueCountFrequency (%)
1.27 1
 
0.7%
1.29 1
 
0.7%
1.3 1
 
0.7%
1.33 3
2.2%
1.42 1
 
0.7%
1.47 1
 
0.7%
1.51 1
 
0.7%
1.55 1
 
0.7%
1.56 2
1.4%
1.58 2
1.4%
ValueCountFrequency (%)
3.92 1
0.7%
3.82 1
0.7%
3.64 1
0.7%
3.59 1
0.7%
3.58 1
0.7%
3.57 1
0.7%
3.55 1
0.7%
3.53 1
0.7%
3.52 1
0.7%
3.5 1
0.7%

proline
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean743.31159
Minimum278
Maximum1515
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-03T14:06:23.972740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum278
5-th percentile379.7
Q1500
median675
Q3981.25
95-th percentile1285.75
Maximum1515
Range1237
Interquartile range (IQR)481.25

Descriptive statistics

Standard deviation302.12539
Coefficient of variation (CV)0.40645859
Kurtosis-0.55806343
Mean743.31159
Median Absolute Deviation (MAD)204
Skewness0.67777701
Sum102577
Variance91279.749
MonotonicityNot monotonic
2024-03-03T14:06:24.371446image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
520 5
 
3.6%
680 4
 
2.9%
750 4
 
2.9%
625 4
 
2.9%
450 3
 
2.2%
510 3
 
2.2%
495 3
 
2.2%
1065 2
 
1.4%
1045 2
 
1.4%
380 2
 
1.4%
Other values (88) 106
76.8%
ValueCountFrequency (%)
278 1
0.7%
325 1
0.7%
342 1
0.7%
345 1
0.7%
352 1
0.7%
365 1
0.7%
378 1
0.7%
380 2
1.4%
385 1
0.7%
392 1
0.7%
ValueCountFrequency (%)
1515 1
0.7%
1480 1
0.7%
1450 1
0.7%
1375 1
0.7%
1320 1
0.7%
1310 1
0.7%
1290 1
0.7%
1285 2
1.4%
1280 2
1.4%
1270 1
0.7%

target
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
1
55 
0
46 
2
37 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters138
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row2

Common Values

ValueCountFrequency (%)
1 55
39.9%
0 46
33.3%
2 37
26.8%

Length

2024-03-03T14:06:24.711673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T14:06:25.019133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 55
39.9%
0 46
33.3%
2 37
26.8%

Most occurring characters

ValueCountFrequency (%)
1 55
39.9%
0 46
33.3%
2 37
26.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 138
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 55
39.9%
0 46
33.3%
2 37
26.8%

Most occurring scripts

ValueCountFrequency (%)
Common 138
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 55
39.9%
0 46
33.3%
2 37
26.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 138
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 55
39.9%
0 46
33.3%
2 37
26.8%

Interactions

2024-03-03T14:06:08.644428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:27.620172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:31.263811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:34.994084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:38.169208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:41.687679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:44.754195image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:47.956616image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:51.586846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:54.708678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:58.279061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:01.679572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:05.280958image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:08.882985image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:27.967323image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:31.501052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:35.288604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:38.408243image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:41.922705image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:44.994484image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:48.224836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:51.842303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:54.947612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:58.551933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:01.929332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:05.561523image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:09.089312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:28.320563image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:31.748347image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:35.526326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:38.633958image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:42.140409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:45.210781image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:48.474815image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:52.117463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:55.241387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:58.852022image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:02.233695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:05.819308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:09.338615image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:28.650784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:32.041252image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:35.769881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:38.882264image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:42.375646image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:45.448152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:48.718320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:52.416543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:55.533789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:59.087891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:02.531529image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:06.111734image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:09.572380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:28.950567image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:32.340335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:36.029776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:39.138901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:42.611914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:45.803333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:48.976542image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:52.668486image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:55.825185image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:59.369926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:02.865843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:06.382582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:09.802444image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:29.233409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:32.571233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:36.257334image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:39.353958image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:42.805467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:46.050261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:49.204798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:52.892644image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:56.075931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:59.601374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:03.145104image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:06.669245image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:10.075161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:29.505072image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:33.174629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:36.499455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:39.587720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:43.032354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:46.276534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:49.439032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:53.125564image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:56.356406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:59.841348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:03.425984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:06.924857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:10.364085image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:29.779962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:33.424975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:36.742811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:39.864575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:43.250656image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:46.501868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:49.694852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:53.358494image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:56.589403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:00.122720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:03.680172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:07.196265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:10.643792image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:30.070238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:33.657180image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:36.984365image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:40.151111image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:43.456561image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:46.747956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:49.934075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:53.569306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:56.855469image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:00.358648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:03.928071image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:07.445534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:10.891054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:30.333218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:33.928779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:37.237774image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:40.431150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:43.680004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:47.005755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:50.199015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:53.805763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:57.134446image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:00.673699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:04.200574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:07.672224image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:11.140111image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:30.564626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:34.235233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:37.458660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:40.759749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:43.979182image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:47.241442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:50.455278image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:54.044899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:57.396552image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:00.947837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:04.484808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:07.899263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:11.985932image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:30.790306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:34.495044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:37.683225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:41.023800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:44.233752image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:47.482032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:50.708881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:54.268077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:57.676245image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:01.187484image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:04.737549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:08.154336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:12.217851image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:31.033496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:34.767176image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:37.917091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:41.361214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:44.525194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:47.715335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:51.310151image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:54.487939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:05:58.013821image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:01.451703image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:04.999308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T14:06:08.383378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-03-03T14:06:25.264087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
alcalinity_of_ashalcoholashcolor_intensityflavanoidshuemagnesiummalic_acidnonflavanoid_phenolsod280/od315_of_diluted_winesproanthocyaninsprolinetargettotal_phenols
alcalinity_of_ash1.000-0.2880.369-0.110-0.422-0.317-0.1880.2230.344-0.334-0.247-0.4680.388-0.329
alcohol-0.2881.0000.2750.6240.326-0.0030.3950.151-0.1600.1320.1600.6240.5730.341
ash0.3690.2751.0000.2800.079-0.0630.3370.2160.138-0.0550.0300.2510.1760.149
color_intensity-0.1100.6240.2801.000-0.043-0.4280.3740.3010.043-0.314-0.0950.4560.6490.010
flavanoids-0.4220.3260.079-0.0431.0000.5230.219-0.279-0.5050.7420.7260.4310.7280.875
hue-0.317-0.003-0.063-0.4280.5231.000-0.001-0.581-0.2310.4710.3770.1980.5820.422
magnesium-0.1880.3950.3370.3740.219-0.0011.0000.125-0.2590.0320.1900.5240.4160.255
malic_acid0.2230.1510.2160.301-0.279-0.5810.1251.0000.201-0.259-0.244-0.0170.498-0.241
nonflavanoid_phenols0.344-0.1600.1380.043-0.505-0.231-0.2590.2011.000-0.504-0.360-0.2470.335-0.409
od280/od315_of_diluted_wines-0.3340.132-0.055-0.3140.7420.4710.032-0.259-0.5041.0000.5620.2730.6150.690
proanthocyanins-0.2470.1600.030-0.0950.7260.3770.190-0.244-0.3600.5621.0000.2890.4210.660
proline-0.4680.6240.2510.4560.4310.1980.524-0.017-0.2470.2730.2891.0000.6340.426
target0.3880.5730.1760.6490.7280.5820.4160.4980.3350.6150.4210.6341.000-0.724
total_phenols-0.3290.3410.1490.0100.8750.4220.255-0.241-0.4090.6900.6600.426-0.7241.000

Missing values

2024-03-03T14:06:12.568271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-03T14:06:13.109964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

alcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesprolinetarget
012.522.432.1721.0882.552.270.261.222.00.902.783251
113.731.502.7022.51013.003.250.292.385.71.192.7112850
213.281.642.8415.51102.602.680.341.364.61.092.788800
313.751.732.4116.0892.602.760.291.815.61.152.9013200
414.341.682.7025.0982.801.310.532.7013.00.571.966602
513.051.732.0412.4922.723.270.172.917.21.122.9111500
612.703.552.3621.51061.701.200.170.845.00.781.296002
714.301.922.7220.01202.803.140.331.976.21.072.6512800
813.055.802.1321.5862.622.650.302.012.60.733.103801
914.121.482.3216.8952.202.430.261.575.01.172.8212800
alcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesprolinetarget
12812.963.452.3518.51061.390.700.400.945.280.681.756752
12911.874.312.3921.0822.863.030.212.912.800.753.643801
13012.882.992.4020.01041.301.220.240.835.400.741.425302
13112.161.612.3122.8901.781.690.431.562.451.332.264951
13212.601.341.9018.5881.451.360.291.352.451.042.775621
13312.081.832.3218.5811.601.500.521.642.401.082.274801
13414.162.512.4820.0911.680.700.441.249.700.621.716602
13513.511.802.6519.01102.352.530.291.544.201.102.8710950
13613.693.262.5420.01071.830.560.500.805.880.961.826802
13712.823.372.3019.5881.480.660.400.9710.260.721.756852